. Current studies are using genome wide scan (GWS) approaches to identify the numerous genes which might play a role in increased risk for psychosis. Structural neuroimaging measures implicate gray matter loss in schizophrenia; a pattern of regional loss in the medial frontal, temporal and insular gyri have been identified by us and others in schizophrenia. However, the recognition that case/control approaches are not perhaps the most useful, has led to an emphasis on cognitive constructs such as attention, memory, language, as in the Research Domain Criteria (RDoC) matrix, to identify cross-diagnostic mechanisms. This has left the psychotic symptoms per se without a clear connection to the neuroanatomical circuits and genetic mechanisms. Identifying the relationships among patterns of gray matter reduction, symptom co-occurrence patterns, and genetic profiles which exist across schizophrenia and bipolar disorder is the goal of this project. We propose a multivariate method for analyzing already existing GWS data, voxelwise measures of gray matter density, and symptom measures from an aggregated dataset of over 4000 individuals with diagnoses from the schizophrenia and bipolar spectrum. We will apply three way parallel ICA, with reference; this technique identifies patterns of spatial variation in the brain structure, symptom profiles, and patterns of genotypes which are linked. We begin with over 7,000 structural imaging, symptom scores, and GWS samples from cases and controls, from aggregated legacy data. We constrain the imaging and genetic analyses with reference vectors to incorporate a priori information.
In Aims 1 and 2 we will develop initial a priori spatial patterns, structural networks using source- based morphometry methods, both alone and in conjunction with symptom measures;
in Aim 3 we will determine the heritability and quantitative trait loci for these networks in independent family samples;
in Aim 4 we use the quantitative trait loci as a priori constraints on the genetic data, and the heritable structural networks as constraints on the imaging data on our three-way parallel ICA analysis. We include a split-half analysis for replication and a follow-up high-density genotyping plan. Using these methods, we will determine the spatial patterns and genetic profiles that covary within our sample, and which show relationships with symptom profiles across schizophrenia and bipolar disorder; this forms the basis for linking the symptoms to the brain circuits and genetics ?units of analysis?. Using higher-order clustering on the identified patterns, we identify coherent sub- groupings of subjects using the genetics, brain structure, and symptom measures within the larger data matrix. The final results will be the combinations of genotypic networks which influence the patterns of structural brain effects in conjunction with variations in symptom clusters, refining the diagnostic categories based on biological evidence.
. The recognition that bipolar disorder and schizophrenia are closely linked phenomenologically, physiologically and genetically, rather than clearly separated disorders, leads us to perform a diagnosis-blind three-way analysis of structural neuroimaging, genome-wide scan, and symptom data on an aggregated dataset of over 4000 individuals with diagnoses of bipolar disorder or schizophrenia. We identify the linked patterns of gray matter effects and symptom presentation across diagnostic groups, distinguishing genetic networks and brain patterns underlying the clinically disparate presentations of positive and negative psychotic symptoms.
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